A Structural Health Monitoring (SHM) system may improve unscheduled and scheduled maintenance operations. An SHM system may advantageously quickly identify occurrence of damage, determine damage location and size, and schedule an appropriate remedial maintenance action. Such an SHM system may reduce maintenance costs.
An SHM system may be employed with an aircraft, by way of example and not by way of limitation, using a plurality of transducers in a transducer array coupled with a structure, such as near the fuselage cargo door of an aircraft where baggage handlers may collide with and cause impact damage to the airplane fuselage, may reduce aircraft schedule cancellations and delays. An SHM system may be particularly advantageous when employed in connection with laminated structures to identify, locate and characterize delamination damage.
An exemplary algorithmic tool for use in evaluating damage to a structure may be an artificial neural network. Neural Networks (NN) may be described as nonlinear algorithms that learn by example. Generally speaking, the smaller the dimension of a training record provided to a NN to effect the learning process by the NN, the fewer records that may be needed for training and the better the NN may be relied upon for generalized conclusions in evaluating real damage to a structure.
For SHM preparation by training an NN, each impact to a structure may produce one training record. Generating more than a few hundred impacts (and thus a few hundred training records) and measuring the response of the transducer array to each impact may be cost prohibitive. If a low number of training records is employed, it may be advisable to provide a limited relatively small number of input factors to an NN associated with an SHM to better assure reliable results.
There is a need for a method and apparatus for creating at least one parameter for algorithmically evaluating damage in a structure that may facilitate accurate evaluation.